Overview

Dataset statistics

Number of variables20
Number of observations5630
Missing cells1856
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory879.8 KiB
Average record size in memory160.0 B

Variable types

Numeric11
Categorical9

Alerts

CustomerID is highly correlated with HourSpendOnAppHigh correlation
HourSpendOnApp is highly correlated with CustomerIDHigh correlation
CouponUsed is highly correlated with OrderCountHigh correlation
OrderCount is highly correlated with CouponUsedHigh correlation
CustomerID is highly correlated with HourSpendOnAppHigh correlation
HourSpendOnApp is highly correlated with CustomerIDHigh correlation
CouponUsed is highly correlated with OrderCountHigh correlation
OrderCount is highly correlated with CouponUsedHigh correlation
CouponUsed is highly correlated with OrderCountHigh correlation
OrderCount is highly correlated with CouponUsedHigh correlation
CustomerID is highly correlated with HourSpendOnApp and 3 other fieldsHigh correlation
PreferredLoginDevice is highly correlated with PreferedOrderCat and 1 other fieldsHigh correlation
CityTier is highly correlated with PreferredPaymentModeHigh correlation
PreferredPaymentMode is highly correlated with CityTierHigh correlation
HourSpendOnApp is highly correlated with CustomerID and 1 other fieldsHigh correlation
NumberOfDeviceRegistered is highly correlated with CustomerID and 1 other fieldsHigh correlation
PreferedOrderCat is highly correlated with PreferredLoginDevice and 1 other fieldsHigh correlation
SatisfactionScore is highly correlated with CustomerIDHigh correlation
MaritalStatus is highly correlated with CustomerIDHigh correlation
CouponUsed is highly correlated with OrderCountHigh correlation
OrderCount is highly correlated with CouponUsedHigh correlation
CashbackAmount is highly correlated with PreferredLoginDevice and 1 other fieldsHigh correlation
Tenure has 264 (4.7%) missing values Missing
WarehouseToHome has 251 (4.5%) missing values Missing
HourSpendOnApp has 255 (4.5%) missing values Missing
OrderAmountHikeFromlastYear has 265 (4.7%) missing values Missing
CouponUsed has 256 (4.5%) missing values Missing
OrderCount has 258 (4.6%) missing values Missing
DaySinceLastOrder has 307 (5.5%) missing values Missing
CustomerID is uniformly distributed Uniform
CustomerID has unique values Unique
Tenure has 508 (9.0%) zeros Zeros
CouponUsed has 1030 (18.3%) zeros Zeros
DaySinceLastOrder has 496 (8.8%) zeros Zeros

Reproduction

Analysis started2022-01-27 11:34:33.857593
Analysis finished2022-01-27 11:34:53.296807
Duration19.44 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

CustomerID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct5630
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52815.5
Minimum50001
Maximum55630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2022-01-27T18:34:53.423107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum50001
5-th percentile50282.45
Q151408.25
median52815.5
Q354222.75
95-th percentile55348.55
Maximum55630
Range5629
Interquartile range (IQR)2814.5

Descriptive statistics

Standard deviation1625.385339
Coefficient of variation (CV)0.03077477897
Kurtosis-1.2
Mean52815.5
Median Absolute Deviation (MAD)1407.5
Skewness0
Sum297351265
Variance2641877.5
MonotonicityStrictly increasing
2022-01-27T18:34:53.548079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500011
 
< 0.1%
537511
 
< 0.1%
537591
 
< 0.1%
537581
 
< 0.1%
537571
 
< 0.1%
537561
 
< 0.1%
537551
 
< 0.1%
537541
 
< 0.1%
537531
 
< 0.1%
537521
 
< 0.1%
Other values (5620)5620
99.8%
ValueCountFrequency (%)
500011
< 0.1%
500021
< 0.1%
500031
< 0.1%
500041
< 0.1%
500051
< 0.1%
500061
< 0.1%
500071
< 0.1%
500081
< 0.1%
500091
< 0.1%
500101
< 0.1%
ValueCountFrequency (%)
556301
< 0.1%
556291
< 0.1%
556281
< 0.1%
556271
< 0.1%
556261
< 0.1%
556251
< 0.1%
556241
< 0.1%
556231
< 0.1%
556221
< 0.1%
556211
< 0.1%

Churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
0
4682 
1
948 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
04682
83.2%
1948
 
16.8%

Length

2022-01-27T18:34:53.688704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T18:34:53.751248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
04682
83.2%
1948
 
16.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Tenure
Real number (ℝ≥0)

MISSING
ZEROS

Distinct36
Distinct (%)0.7%
Missing264
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean10.18989937
Minimum0
Maximum61
Zeros508
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2022-01-27T18:34:53.829346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q316
95-th percentile27
Maximum61
Range61
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.557240984
Coefficient of variation (CV)0.8397767904
Kurtosis-0.007369469517
Mean10.18989937
Median Absolute Deviation (MAD)7
Skewness0.7365133839
Sum54679
Variance73.22637326
MonotonicityNot monotonic
2022-01-27T18:34:53.939667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1690
 
12.3%
0508
 
9.0%
8263
 
4.7%
9247
 
4.4%
7221
 
3.9%
10213
 
3.8%
5204
 
3.6%
4203
 
3.6%
3195
 
3.5%
11194
 
3.4%
Other values (26)2428
43.1%
(Missing)264
 
4.7%
ValueCountFrequency (%)
0508
9.0%
1690
12.3%
2167
 
3.0%
3195
 
3.5%
4203
 
3.6%
5204
 
3.6%
6183
 
3.3%
7221
 
3.9%
8263
 
4.7%
9247
 
4.4%
ValueCountFrequency (%)
611
 
< 0.1%
601
 
< 0.1%
511
 
< 0.1%
501
 
< 0.1%
3149
0.9%
3066
1.2%
2955
1.0%
2870
1.2%
2766
1.2%
2660
1.1%

PreferredLoginDevice
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Mobile Phone
2765 
Computer
1634 
Phone
1231 

Length

Max length12
Median length8
Mean length9.308525755
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile Phone
2nd rowPhone
3rd rowPhone
4th rowPhone
5th rowPhone

Common Values

ValueCountFrequency (%)
Mobile Phone2765
49.1%
Computer1634
29.0%
Phone1231
21.9%

Length

2022-01-27T18:34:54.064668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T18:34:54.150374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
phone3996
47.6%
mobile2765
32.9%
computer1634
19.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CityTier
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
1
3666 
3
1722 
2
 
242

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
13666
65.1%
31722
30.6%
2242
 
4.3%

Length

2022-01-27T18:34:54.219791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T18:34:54.295344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
13666
65.1%
31722
30.6%
2242
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WarehouseToHome
Real number (ℝ≥0)

MISSING

Distinct34
Distinct (%)0.6%
Missing251
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean15.63989589
Minimum5
Maximum127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2022-01-27T18:34:54.403842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median14
Q320
95-th percentile33
Maximum127
Range122
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.531475187
Coefficient of variation (CV)0.545494372
Kurtosis9.986930421
Mean15.63989589
Median Absolute Deviation (MAD)5
Skewness1.619153668
Sum84127
Variance72.78606886
MonotonicityNot monotonic
2022-01-27T18:34:54.556077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
9559
 
9.9%
8444
 
7.9%
7389
 
6.9%
16322
 
5.7%
14299
 
5.3%
6295
 
5.2%
15288
 
5.1%
10274
 
4.9%
13249
 
4.4%
11233
 
4.1%
Other values (24)2027
36.0%
(Missing)251
 
4.5%
ValueCountFrequency (%)
58
 
0.1%
6295
5.2%
7389
6.9%
8444
7.9%
9559
9.9%
10274
4.9%
11233
4.1%
12221
 
3.9%
13249
4.4%
14299
5.3%
ValueCountFrequency (%)
1271
 
< 0.1%
1261
 
< 0.1%
3651
0.9%
3593
1.7%
3463
1.1%
3367
1.2%
3294
1.7%
31101
1.8%
3094
1.7%
2981
1.4%

PreferredPaymentMode
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Debit Card
2314 
Credit Card
1501 
E wallet
614 
UPI
414 
COD
365 
Other values (2)
422 

Length

Max length16
Median length10
Mean length8.85079929
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDebit Card
2nd rowUPI
3rd rowDebit Card
4th rowDebit Card
5th rowCC

Common Values

ValueCountFrequency (%)
Debit Card2314
41.1%
Credit Card1501
26.7%
E wallet614
 
10.9%
UPI414
 
7.4%
COD365
 
6.5%
CC273
 
4.8%
Cash on Delivery149
 
2.6%

Length

2022-01-27T18:34:54.701231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T18:34:54.808269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
card3815
36.8%
debit2314
22.3%
credit1501
 
14.5%
e614
 
5.9%
wallet614
 
5.9%
upi414
 
4.0%
cod365
 
3.5%
cc273
 
2.6%
cash149
 
1.4%
on149
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Male
3384 
Female
2246 

Length

Max length6
Median length4
Mean length4.797868561
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male3384
60.1%
Female2246
39.9%

Length

2022-01-27T18:34:54.935187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T18:34:55.015224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
male3384
60.1%
female2246
39.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HourSpendOnApp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.1%
Missing255
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean2.931534884
Minimum0
Maximum5
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2022-01-27T18:34:55.073514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72192585
Coefficient of variation (CV)0.2462620704
Kurtosis-0.667076137
Mean2.931534884
Median Absolute Deviation (MAD)1
Skewness-0.02721262163
Sum15757
Variance0.5211769329
MonotonicityNot monotonic
2022-01-27T18:34:55.168311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
32687
47.7%
21471
26.1%
41176
20.9%
135
 
0.6%
03
 
0.1%
53
 
0.1%
(Missing)255
 
4.5%
ValueCountFrequency (%)
03
 
0.1%
135
 
0.6%
21471
26.1%
32687
47.7%
41176
20.9%
53
 
0.1%
ValueCountFrequency (%)
53
 
0.1%
41176
20.9%
32687
47.7%
21471
26.1%
135
 
0.6%
03
 
0.1%

NumberOfDeviceRegistered
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.688987567
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2022-01-27T18:34:55.262092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.023998519
Coefficient of variation (CV)0.2775825346
Kurtosis0.5828487316
Mean3.688987567
Median Absolute Deviation (MAD)1
Skewness-0.3969686435
Sum20769
Variance1.048572967
MonotonicityNot monotonic
2022-01-27T18:34:55.355840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
42377
42.2%
31699
30.2%
5881
 
15.6%
2276
 
4.9%
1235
 
4.2%
6162
 
2.9%
ValueCountFrequency (%)
1235
 
4.2%
2276
 
4.9%
31699
30.2%
42377
42.2%
5881
 
15.6%
6162
 
2.9%
ValueCountFrequency (%)
6162
 
2.9%
5881
 
15.6%
42377
42.2%
31699
30.2%
2276
 
4.9%
1235
 
4.2%

PreferedOrderCat
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Laptop & Accessory
2050 
Mobile Phone
1271 
Fashion
826 
Mobile
809 
Grocery
410 

Length

Max length18
Median length12
Mean length11.94351687
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaptop & Accessory
2nd rowMobile
3rd rowMobile
4th rowLaptop & Accessory
5th rowMobile

Common Values

ValueCountFrequency (%)
Laptop & Accessory2050
36.4%
Mobile Phone1271
22.6%
Fashion826
14.7%
Mobile809
 
14.4%
Grocery410
 
7.3%
Others264
 
4.7%

Length

2022-01-27T18:34:55.481626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T18:34:55.555609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
mobile2080
18.9%
laptop2050
18.6%
2050
18.6%
accessory2050
18.6%
phone1271
11.6%
fashion826
 
7.5%
grocery410
 
3.7%
others264
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SatisfactionScore
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
3
1698 
1
1164 
5
1108 
4
1074 
2
586 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row5
5th row5

Common Values

ValueCountFrequency (%)
31698
30.2%
11164
20.7%
51108
19.7%
41074
19.1%
2586
 
10.4%

Length

2022-01-27T18:34:55.649332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T18:34:55.727479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
31698
30.2%
11164
20.7%
51108
19.7%
41074
19.1%
2586
 
10.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MaritalStatus
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Married
2986 
Single
1796 
Divorced
848 

Length

Max length8
Median length7
Mean length6.831616341
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowSingle

Common Values

ValueCountFrequency (%)
Married2986
53.0%
Single1796
31.9%
Divorced848
 
15.1%

Length

2022-01-27T18:34:55.836811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T18:34:55.914983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
married2986
53.0%
single1796
31.9%
divorced848
 
15.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NumberOfAddress
Real number (ℝ≥0)

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.214031972
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2022-01-27T18:34:55.977439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile10
Maximum22
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.583585513
Coefficient of variation (CV)0.6130911037
Kurtosis0.9592292732
Mean4.214031972
Median Absolute Deviation (MAD)1
Skewness1.088639383
Sum23725
Variance6.674914101
MonotonicityNot monotonic
2022-01-27T18:34:56.086815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
21369
24.3%
31278
22.7%
4588
10.4%
5571
10.1%
6382
 
6.8%
1371
 
6.6%
8280
 
5.0%
7256
 
4.5%
9239
 
4.2%
10194
 
3.4%
Other values (5)102
 
1.8%
ValueCountFrequency (%)
1371
 
6.6%
21369
24.3%
31278
22.7%
4588
10.4%
5571
10.1%
6382
 
6.8%
7256
 
4.5%
8280
 
5.0%
9239
 
4.2%
10194
 
3.4%
ValueCountFrequency (%)
221
 
< 0.1%
211
 
< 0.1%
201
 
< 0.1%
191
 
< 0.1%
1198
 
1.7%
10194
3.4%
9239
4.2%
8280
5.0%
7256
4.5%
6382
6.8%

Complain
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
0
4026 
1
1604 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04026
71.5%
11604
 
28.5%

Length

2022-01-27T18:34:56.180571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-27T18:34:56.243101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
04026
71.5%
11604
 
28.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

OrderAmountHikeFromlastYear
Real number (ℝ≥0)

MISSING

Distinct16
Distinct (%)0.3%
Missing265
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean15.70792171
Minimum11
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2022-01-27T18:34:56.306704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q113
median15
Q318
95-th percentile23
Maximum26
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.675485463
Coefficient of variation (CV)0.2339892908
Kurtosis-0.2803811889
Mean15.70792171
Median Absolute Deviation (MAD)3
Skewness0.7907853591
Sum84273
Variance13.50919339
MonotonicityNot monotonic
2022-01-27T18:34:56.403132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
14750
13.3%
13741
13.2%
12728
12.9%
15542
9.6%
11391
6.9%
16333
5.9%
18321
5.7%
19311
5.5%
17297
 
5.3%
20243
 
4.3%
Other values (6)708
12.6%
(Missing)265
 
4.7%
ValueCountFrequency (%)
11391
6.9%
12728
12.9%
13741
13.2%
14750
13.3%
15542
9.6%
16333
5.9%
17297
 
5.3%
18321
5.7%
19311
5.5%
20243
 
4.3%
ValueCountFrequency (%)
2633
 
0.6%
2573
 
1.3%
2484
 
1.5%
23144
2.6%
22184
3.3%
21190
3.4%
20243
4.3%
19311
5.5%
18321
5.7%
17297
5.3%

CouponUsed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct17
Distinct (%)0.3%
Missing256
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean1.751023446
Minimum0
Maximum16
Zeros1030
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2022-01-27T18:34:56.512507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile6
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.894621447
Coefficient of variation (CV)1.08200804
Kurtosis9.132281171
Mean1.751023446
Median Absolute Deviation (MAD)1
Skewness2.545652562
Sum9410
Variance3.589590428
MonotonicityNot monotonic
2022-01-27T18:34:56.621876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
12105
37.4%
21283
22.8%
01030
18.3%
3327
 
5.8%
4197
 
3.5%
5129
 
2.3%
6108
 
1.9%
789
 
1.6%
842
 
0.7%
1014
 
0.2%
Other values (7)50
 
0.9%
(Missing)256
 
4.5%
ValueCountFrequency (%)
01030
18.3%
12105
37.4%
21283
22.8%
3327
 
5.8%
4197
 
3.5%
5129
 
2.3%
6108
 
1.9%
789
 
1.6%
842
 
0.7%
913
 
0.2%
ValueCountFrequency (%)
162
 
< 0.1%
151
 
< 0.1%
145
 
0.1%
138
 
0.1%
129
 
0.2%
1112
 
0.2%
1014
 
0.2%
913
 
0.2%
842
0.7%
789
1.6%

OrderCount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)0.3%
Missing258
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean3.008004468
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2022-01-27T18:34:56.731269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.939679548
Coefficient of variation (CV)0.9772856323
Kurtosis4.718466052
Mean3.008004468
Median Absolute Deviation (MAD)1
Skewness2.196414108
Sum16159
Variance8.641715846
MonotonicityNot monotonic
2022-01-27T18:34:56.825000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
22025
36.0%
11751
31.1%
3371
 
6.6%
7206
 
3.7%
4204
 
3.6%
5181
 
3.2%
8172
 
3.1%
6137
 
2.4%
962
 
1.1%
1254
 
1.0%
Other values (6)209
 
3.7%
(Missing)258
 
4.6%
ValueCountFrequency (%)
11751
31.1%
22025
36.0%
3371
 
6.6%
4204
 
3.6%
5181
 
3.2%
6137
 
2.4%
7206
 
3.7%
8172
 
3.1%
962
 
1.1%
1036
 
0.6%
ValueCountFrequency (%)
1623
 
0.4%
1533
 
0.6%
1436
 
0.6%
1330
 
0.5%
1254
 
1.0%
1151
 
0.9%
1036
 
0.6%
962
 
1.1%
8172
3.1%
7206
3.7%

DaySinceLastOrder
Real number (ℝ≥0)

MISSING
ZEROS

Distinct22
Distinct (%)0.4%
Missing307
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean4.543490513
Minimum0
Maximum46
Zeros496
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2022-01-27T18:34:57.191568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum46
Range46
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.654433197
Coefficient of variation (CV)0.8043228409
Kurtosis4.023964341
Mean4.543490513
Median Absolute Deviation (MAD)2
Skewness1.190999503
Sum24185
Variance13.35488199
MonotonicityNot monotonic
2022-01-27T18:34:57.285336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3900
16.0%
2792
14.1%
1614
10.9%
8538
9.6%
0496
8.8%
7447
7.9%
4431
7.7%
9299
 
5.3%
5228
 
4.0%
10157
 
2.8%
Other values (12)421
7.5%
(Missing)307
 
5.5%
ValueCountFrequency (%)
0496
8.8%
1614
10.9%
2792
14.1%
3900
16.0%
4431
7.7%
5228
 
4.0%
6113
 
2.0%
7447
7.9%
8538
9.6%
9299
 
5.3%
ValueCountFrequency (%)
461
 
< 0.1%
311
 
< 0.1%
301
 
< 0.1%
1810
 
0.2%
1717
 
0.3%
1613
 
0.2%
1519
 
0.3%
1435
0.6%
1351
0.9%
1269
1.2%

CashbackAmount
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2586
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.2230302
Minimum0
Maximum324.99
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2022-01-27T18:34:57.425959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile123.0335
Q1145.77
median163.28
Q3196.3925
95-th percentile291.9385
Maximum324.99
Range324.99
Interquartile range (IQR)50.6225

Descriptive statistics

Standard deviation49.20703617
Coefficient of variation (CV)0.2776559916
Kurtosis0.9745052177
Mean177.2230302
Median Absolute Deviation (MAD)23.035
Skewness1.149845719
Sum997765.66
Variance2421.332409
MonotonicityNot monotonic
2022-01-27T18:34:57.550945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123.428
 
0.1%
149.368
 
0.1%
148.428
 
0.1%
188.477
 
0.1%
154.737
 
0.1%
180.626
 
0.1%
126.16
 
0.1%
125.596
 
0.1%
153.046
 
0.1%
146.276
 
0.1%
Other values (2576)5562
98.8%
ValueCountFrequency (%)
04
0.1%
121
 
< 0.1%
254
0.1%
371
 
< 0.1%
561
 
< 0.1%
811
 
< 0.1%
110.092
< 0.1%
110.512
< 0.1%
110.522
< 0.1%
110.812
< 0.1%
ValueCountFrequency (%)
324.992
< 0.1%
324.732
< 0.1%
324.432
< 0.1%
324.262
< 0.1%
323.592
< 0.1%
323.472
< 0.1%
323.452
< 0.1%
323.332
< 0.1%
322.42
< 0.1%
322.172
< 0.1%

Interactions

2022-01-27T18:34:50.838235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:35.600188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:37.014068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:38.404052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:39.930527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:41.381183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:42.915778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:44.743259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:46.272960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:47.756727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:49.261398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:50.960150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:35.730358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:37.137765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:38.525779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:40.054173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:41.514131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:43.047384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:44.879485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:46.414485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:47.879143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:49.378118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:51.103688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:35.870733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:37.272822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:38.651004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:40.179178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:41.649888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:43.172048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:45.010470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:46.546325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:48.021280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:49.499528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:51.241820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:35.995490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:37.391726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:38.776299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:40.333148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:41.781244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:43.292022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:45.148722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:46.674607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:48.162252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:49.617519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:51.368646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:36.115750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:37.514410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:38.897412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:40.485926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:41.931419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:43.411311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:45.287287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:46.797081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:48.282300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:49.720820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:51.491425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:36.245433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:37.639194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:39.025764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:40.611485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:42.072426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:43.537286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:45.432283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:46.954096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:48.411281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:49.888130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:51.592007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:36.363569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:37.751517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:39.158762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:40.726826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:42.195255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:44.090984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:45.568283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:47.094659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:48.557279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:50.016043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:51.741049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:36.496011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:37.878742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:39.340925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:40.871240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:42.349245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:44.250492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:45.711067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:47.231009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:48.705610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:50.158585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:51.880316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:36.625512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:38.010710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:39.524822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:41.013187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:42.491722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:44.378864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:45.863292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:47.361339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:48.842044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:50.292555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:52.009406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:36.750804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:38.143224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:39.664858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:41.138081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:42.623246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:44.507174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:46.004581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:47.505603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:48.982383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:50.423206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:52.132426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:36.875472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:38.261909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:39.799859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:41.259296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:42.748148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:44.622681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:46.132499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:47.628582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:49.128029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-27T18:34:50.708749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-01-27T18:34:57.675988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-27T18:34:57.900095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-27T18:34:58.125373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-27T18:34:58.344133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-27T18:34:58.516008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-27T18:34:52.373726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-27T18:34:52.771296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-27T18:34:52.991570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-27T18:34:53.153230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CustomerIDChurnTenurePreferredLoginDeviceCityTierWarehouseToHomePreferredPaymentModeGenderHourSpendOnAppNumberOfDeviceRegisteredPreferedOrderCatSatisfactionScoreMaritalStatusNumberOfAddressComplainOrderAmountHikeFromlastYearCouponUsedOrderCountDaySinceLastOrderCashbackAmount
05000114.0Mobile Phone36.0Debit CardFemale3.03Laptop & Accessory2Single9111.01.01.05.0159.93
1500021NaNPhone18.0UPIMale3.04Mobile3Single7115.00.01.00.0120.90
2500031NaNPhone130.0Debit CardMale2.04Mobile3Single6114.00.01.03.0120.28
35000410.0Phone315.0Debit CardMale2.04Laptop & Accessory5Single8023.00.01.03.0134.07
45000510.0Phone112.0CCMaleNaN3Mobile5Single3011.01.01.03.0129.60
55000610.0Computer122.0Debit CardFemale3.05Mobile Phone5Single2122.04.06.07.0139.19
6500071NaNPhone311.0Cash on DeliveryMale2.03Laptop & Accessory2Divorced4014.00.01.00.0120.86
7500081NaNPhone16.0CCMale3.03Mobile2Divorced3116.02.02.00.0122.93
850009113.0Phone39.0E walletMaleNaN4Mobile3Divorced2114.00.01.02.0126.83
9500101NaNPhone131.0Debit CardMale2.05Mobile3Single2012.01.01.01.0122.93

Last rows

CustomerIDChurnTenurePreferredLoginDeviceCityTierWarehouseToHomePreferredPaymentModeGenderHourSpendOnAppNumberOfDeviceRegisteredPreferedOrderCatSatisfactionScoreMaritalStatusNumberOfAddressComplainOrderAmountHikeFromlastYearCouponUsedOrderCountDaySinceLastOrderCashbackAmount
56205562103.0Mobile Phone135.0Credit CardFemale4.05Mobile Phone5Single3015.01.02.05.0162.85
562155622114.0Mobile Phone335.0E walletMale3.05Fashion5Married6114.03.0NaN1.0233.54
562255623013.0Mobile Phone331.0E walletFemale3.05Grocery1Married2012.04.0NaN7.0245.31
56235562405.0Computer112.0Credit CardMale4.04Laptop & Accessory5Single2020.02.02.0NaN224.36
56245562501.0Mobile Phone312.0UPIFemale2.05Mobile Phone3Single2019.02.02.01.0154.66
562555626010.0Computer130.0Credit CardMale3.02Laptop & Accessory1Married6018.01.02.04.0150.71
562655627013.0Mobile Phone113.0Credit CardMale3.05Fashion5Married6016.01.02.0NaN224.91
56275562801.0Mobile Phone111.0Debit CardMale3.02Laptop & Accessory4Married3121.01.02.04.0186.42
562855629023.0Computer39.0Credit CardMale4.05Laptop & Accessory4Married4015.02.02.09.0178.90
56295563008.0Mobile Phone115.0Credit CardMale3.02Laptop & Accessory3Married4013.02.02.03.0169.04